Impressive predictive analytics coupled with interactive data discovery technology enable rational SAS analysts to distinguish pertinent trends and interactions in datasets, and pan out questions from all dimensions. This smashing concoction of technologies also allows business users to exchange ideas with pundits, to create, modify and pick the best predictive models, constructively.
A comprehensive SAS solution might be the key to empower users in taking better business decisions, without wasting much time. This kind of interactive solution must involve ceaseless communication, giving enough room to even non-technical users to explore data visually, develop analytic models, and share fruitful results.
In this blog to understand in depth how predictive analytics and interactive data discovery help solve day-to-day business issues, we’re going to create three fictional employee characters and observe how they analyze data without preconceived notions, give birth to new ideas and visualize significant observations – along with sharing these insights with their teammates.
Customer Churn in Marketing Analytics
Our first character, Barkha is a marketing manager working for a European retail giant. She has been given the responsibility to point out where the problem of customer churn lies. Previously, she only got scope to work on historical data – gawk at pounds of monthly reports related to previous customers, who no longer purchases from her company store.
Now, by introducing interactive data discovery, Barkha gain access to newer insights from the previous data, while exploring data in various new ways. This intensive exploration helps her determine new trends and interactions that were impossible before – it also provides her with new variables related to customer churn.
As a result, the solution is more rewarding, in fact twice in effect, when merged with predictive analytics. She can now implement logistic regression and other decision tree techniques to forecast the probability of customer churn, which would do wonders for her and her company.
Credit Risk Modeling backed by Predictive Analytics
Vikas, our second character heads a team of risk analysts at a top notch overseas mortgage firm that provides services to masses in North America. Though their time-honored credit risk models were fetching good results, they stupendously failed when it came to predicting mortgage defaults.
The latest data discovery and analytics solution mixture allows Vikas’ team to pinpoint accurately the customers who default on their mortgages. They work by evaluating data from multiple sources and putting into use the boons of predictive analytics to hatch more comprehensive customer profiles.
Self-Service Analytics
Rakshit, being a chief engineer for a notable Indian Energy and Power company used to send a technician to repair equipment in case of emergencies. With the advent of self-service analytics, he is now empowered by an expertise to investigate the factors that resulted into equipment failure. His mode of work includes jotting down a decision tree to highlight past failures in order to predict future failures. The fetching interactive capabilities enable him to outgrow his decision tree to a desired level of detailing, and then compare his predictive model to a logistic regression model with a sole aim to find out which is more effective.
In conclusion, the character assessment of these three individuals has put us into a juncture where we can proudly say that interactive data discovery and analytics has made business users and analysts extremely happy and content. With the power of data in their hands; they just need to make better and smarter data-driven business decisions and strike all the right chords of success!
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